Robotic sensing apparatus and methods of sensor planning

ABSTRACT

The present disclosure is directed to a computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors. The computer-implemented method includes defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.

FIELD

The present disclosure is related generally to robotic sensing devicesand methods of sensor planning.

BACKGROUND

Sensor planning is a general requirement in inspections, measurements,and robot localization, navigation, or mapping relative to an area ofinterest. Areas of interest may include a component, part, detail,assembly, or spatial area, such as a geographic area or other 2D or 3Dspace. Additionally, a general requirement of inspection and measurementmethods, and autonomous robotics, is to employ sensors to capturesamples, such as images or measurements, of the area of interest insufficient detail and a desired level of completeness.

A known solution for sensor planning is to utilize manually programmedsensor plans, such as coordinate systems, routes, or pathways, forcapturing the desired area of interest. However, manually programmedsensor plans often require unchanging and/or substantially certain areasof interest. Therefore, areas of interest that deviate from thepreprogrammed plan often result in sampling errors, omissions, or otherfailures.

Another known solution for sensor planning may include programming arobot to capture large quantities of samples to ensure the area ofinterest is captured in sufficient detail and a desired level ofcompleteness. However, capturing large quantities of samples issimilarly costly, time consuming, and results in inefficient quantitiesof redundant samples. Additionally, the unknown nominal areas ofinterest, or changes to the area of interest relative to nominal, maysimilarly result in errors, omissions, or failures to capture thedesired area of interest.

Therefore, there exists a need for robotic sensing systems and methodsof sensor planning that may capture samples of the desired and/orpotentially unknown or changing area of interest in sufficient detailand completeness while minimizing redundancy and time.

BRIEF DESCRIPTION

Aspects and advantages of the invention will be set forth in part in thefollowing description, or may be obvious from the description, or may belearned through practice of the invention.

The present disclosure is directed to a computer-implemented method ofsensor planning for acquiring samples via an apparatus including one ormore sensors. The computer-implemented method includes defining, by oneor more computing devices, an area of interest; identifying, by the oneor more computing devices, one or more sensing parameters for the one ormore sensors; determining, by the one or more computing devices, asampling combination for acquiring a plurality of samples by the one ormore sensors based at least in part on the one or more sensingparameters; and providing, by the one or more computing devices, one ormore command control signals to the apparatus including the one or moresensors to acquire the plurality of samples of the area of interestusing the one or more sensors based at least on the samplingcombination.

A further aspect of the present disclosure is directed to a roboticsensing apparatus for sensor planning. The apparatus includes one ormore sensors and a computing device, in which the computing deviceincludes one or more processors and one or more memory devices. The oneor more memory devices store instructions that when executed by the oneor more processors cause the one or more processors to performoperations. The operations include receiving an area of interest;receiving one or more sensing parameters for the one or more sensors;determining a sampling combination for acquiring a plurality of samplesby the one or more sensors; and acquiring the plurality of samples usingthe one or more sensors based at least on the sampling combination.

A still further aspect of the present disclosure is directed to anapparatus for sensor planning. The apparatus includes a translatingrobotic apparatus, one or more sensors mounted to the translatingrobotic apparatus, and one or more computing devices configured tooperate the translating robotic apparatus and the one or more sensors.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the invention and, together with the description, serveto explain the principles of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

A full and enabling disclosure of the present invention, including thebest mode thereof, directed to one of ordinary skill in the art, is setforth in the specification, which makes reference to the appendedfigures, in which:

FIG. 1 is an exemplary embodiment of a robotic sensing apparatus;

FIG. 2 is an exemplary embodiment of another robotic sensing apparatus;

FIG. 3 is a flowchart outlining an exemplary method of sensor planning;and

FIG. 4 is an exemplary embodiment of yet another robotic sensingapparatus.

Repeat use of reference characters in the present specification anddrawings is intended to represent the same or analogous features orelements of the present invention.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments of the invention,one or more examples of which are illustrated in the drawings. Eachexample is provided by way of explanation of the invention, notlimitation of the invention. In fact, it will be apparent to thoseskilled in the art that various modifications and variations can be madein the present invention without departing from the scope or spirit ofthe invention. For instance, features illustrated or described as partof one embodiment can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that the present inventioncovers such modifications and variations as come within the scope of theappended claims and their equivalents.

As used herein, the terms “first”, “second”, and “third” may be usedinterchangeably to distinguish one component from another and are notintended to signify location or importance of the individual components.

Robotic sensing apparatuses and methods of sensor planning that maycapture samples of an area of interest while minimizing redundancy andtime are generally provided. The methods and systems described hereinmay include steps or operations that may capture samples of an area ofinterest, such as a component, or assembly, or geographic area, at adesired resolution and level of completeness while minimizing thequantity of samples, such as images or measurements, taken to capturethe area of interest. Various embodiments of the robotic sensingapparatuses and methods described herein may utilize a deep learningapproach in conjunction with sensor planning. Furthermore, the systemsand methods described herein may generally autonomously plan and capturea minimal quantity of samples to capture the area of interest at adesired level of completeness.

Referring now to FIGS. 1 and 2, a robotic sensing apparatus for sensorplanning 90 (herein referred to as “apparatus 90”) includes one or moresensors 110 acquiring samples of an area of interest 130. The one ormore sensors 110 may include an imaging device, a proximity sensor, orcombinations thereof. In one embodiment, imaging devices may generallyinclude cameras. In another embodiment, imaging devices may specificallyinclude interferometers, such as, but not limited to, optical coherencetomography (e.g., white light scanners or blue light scanners). In otherembodiments, the one or more sensors 110 include proximity sensors, inwhich the proximity sensors may generally include sensors that may emitand/or retrieve electromagnetic signals and process changes in saidelectromagnetic signals. For example, proximity sensors may include, butare not limited to, capacitive, infrared, inductive, magnetic, sonic orultrasonic proximity sensors, radar, LIDAR, or laser rangefinders. Invarious embodiments, the one or more sensors 110 may includecombinations of imaging devices and/or proximity sensors. In variousembodiments, the one or more sensors 110 acquire samples, includingimages or measurements, at various resolutions, angles, distances,orientations, sampling or measurement rates, frequencies, etc.

In one embodiment, the apparatus 90 includes a translatable roboticapparatus 100 (herein referred to as “robot 100”). The robot 100 mayinclude a movable fixture, such as a robotic arm as shown in FIGS. 1 and2, or an autonomous mobile vehicle, such as a drone as shown in FIG. 4.Translations of the robot 100, the one or more sensors 110, and/or thearea of interest 130 may include, but are not limited to, six-axismovements (e.g. up/down, side/side, forward/backward, etc.), pivots,turns, rotations, and/or displacements at constant or variable rates ofmotion. In the embodiments shown in FIGS. 1 and 2, the sensor(s) 110 maybe mounted to the robot 100 in which the robot 100 translates the sensor110 to various portions 131 of an area of interest 130 at various anglesor distances relative to the area of interest 130.

In other embodiments of the apparatus 90, the robot 100 may translatethe area of interest 130 relative to the one or more sensors 110. Forexample, the robot 100, such as a robotic arm, may translate the area ofinterest 130 relative to one or more fixed sensors 110. The robot 100may translate the area of interest 130 to various distances, angles,and/or orientations relative to the one or more sensors 110.

Referring now to FIG. 3, a flowchart outlining steps of an exemplaryembodiment of a method of sensor planning 300 (herein referred to as“method 300”) is generally provided. The method 300 shown in FIG. 3 maybe implemented by the apparatus 90 shown and described in regard toFIGS. 1 and 2. The method 300 may further be implemented by one or morecomputing devices, such as the computing device 120 described in regardto FIG. 4. FIG. 3 depicts steps performed in a particular order forpurposes of illustration and discussion. Those of ordinary skill in theart, using the disclosures provided herein, will understand that varioussteps of any of the methods disclosed herein can be modified, adapted,expanded, rearranged and/or omitted in various ways without deviatingfrom the scope of the present disclosure.

The method 300 can include at (310) defining, by one or more computingdevices, an area of interest, at (320) identifying, by the one or morecomputing devices, one or more sensing parameters for the one or moresensors, at (330) determining, by the one or more computing devices, asampling combination for acquiring a plurality of samples by the one ormore sensors based at least in part on the one or more sensingparameters, and at (340) providing, by one or more computing devices,one or more command control signals to an apparatus including the one ormore sensors to acquire the plurality of samples of the area of interestusing the one or more sensors based at least on the samplingcombination.

At (310), the method 300 may include defining an area of interest. Inone embodiment, defining an area of interest includes receiving a pointcloud. Receiving a point cloud may include receiving an image file, suchas a computer-aided design (CAD) file, of the area of interest. Theimage file may include a nominal file of the area of interest to whichthe samples from the sensors may measure in comparison.

In another embodiment, defining an area of interest may include defininga finite space in which a robot and/or one or more sensors may operate,such as the robot 100 and/or one or more sensors 110 shown in FIGS. 1,2, and 4. For example, the extent to which the robot 100 may translatemay be spatially limited. In one example, the robot 100, as a roboticarm, may be limited in its range of motion, extension, etc. In anotherexample, the robot 100, as a drone, may be geographically limited bycoordinates, operating range, or operating envelope, such as altitude,speed, maneuverability, etc. Therefore, defining an area of interest mayinclude defining a 2D or 3D space in which samples may be taken.

In still other embodiments, defining an area of interest may includetaking a sample of the area of interest. For example, taking a sample ofthe area of interest may include taking a sample that broadly capturesthe area of interest, including a perimeter of the area of interest.Broadly capturing the area of interest may include sampling at a lowresolution, or a large distance from the area of interest, or otherwisein minimal detail to obtain and define a periphery of the area ofinterest. In various embodiments, broadly capturing the area of interestmay include capturing the defined spatial area as limited bycoordinates, operating range, operating envelope, etc. In otherembodiments, broadly capturing the area of interest may be dependent ona maximum sampling area of one or more sensors such that the area ofinterest may be defined by the one or more sensing parameters.

At (320), the method 300 includes identifying one or more sensingparameters for the one or more sensors. In one embodiment, identifyingone or more sensing parameters may include defining one or more of ameasurement resolution, a field of view, and/or a depth of field.Defining the measurement resolution may include defining a lateralresolution, a lens resolution, an image resolution, and/or a sensorresolution. Defining a sensor resolution may include defining a spatialand/or temporal sensor resolution.

In another embodiment, identifying one or more sensing parameters mayinclude defining a total area covered by the one or more sensors. Forexample, defining a total area covered by the one or more sensors may bea function of one or more of the defined aforementioned resolutions. Asanother non-limiting example, such as shown and described in regard toFIGS. 1, 2, and 4, defining a total area covered may be approximatelyequal to or less than the portion 131 of the area of interest 130captured by the one or more sensors 110. In one embodiment, the totalarea covered by the one or more sensors may be a function of one or moreof the defined aforementioned resolutions and additional user-definedlimits based at least on a desired sample quality. For example, definingthe total area covered may include defined hardware capabilities of theone or more sensors and a subset of said hardware capabilities based atleast on a user-defined limitation. The user-defined limitation may bebased generally on good visibility standards as defined by the user.Good visibility standards may be based at least on a measurementresolution, a field of view, and/or depth of field of the one or moresensors.

In still other embodiments at (320), identifying one or more sensingparameters may include calculating a curvature and/or normal vector ofat least a portion of the area of interest. Calculating the curvatureand/or normal vector may be based at least on a surface of the pointcloud or image file defined in (310). The normal vector may define oneor more sensor centers. For example, referring to FIGS. 1 and 2, thenormal vector may define one or more centerlines 111 of the one or moresensors 110 relative to the portion 131 of the area of interest 130. Asanother non-limiting example, the normal vector may define an angle ofview 112 of the sensor 110 relative to the area of interest 130, or theportion 131 thereof.

At (330), the method 300 includes determining a sampling combination foracquiring a plurality of samples by the one or more sensors based atleast in part on the one or more sensing parameters. The samplingcombination may be a combination of samples of the area of interestpursuant to capturing the area of interest. The combination of samplesof the area of interest may include translations of the sensor(s) and/orthe area of interest relative to one another. The sampling combinationmay further be a combination of sensing parameters relative totranslations of the sensor(s) and/or the area of interest. In variousembodiments, the sampling combination may include combinations ofsamples of various portions of the area of interest taken to capture thearea of interest. For example, referring to FIG. 1, 2 or 4, the samplingcombination may include a specific sequence of translations and/orsensing parameters at various portions 131 of the area of interest 130until the area of interest 130 is captured. The specific sequence oftranslations and/or sensing parameters may include distances, angles,and/or resolutions of the one or more sensor 110 relative to the area ofinterest 130 for each sample captured of the portion 131 of the area ofinterest 130.

Determining a sampling combination to be acquired by the one or moresensors may further include determining a minimal quantity of samples toacquire to capture the area of interest. In one embodiment, determininga sampling combination to be acquired by the one or more sensors mayinclude selecting the sampling combination based at least on a scorefunction,

${F_{\lambda}\left( {c_{0},c_{1},\ldots\mspace{14mu},c_{r}} \right)} = \frac{{Total}\mspace{14mu}{area}\mspace{14mu}{covered}}{\left( {{Overlap}\mspace{14mu}{Perimeter}} \right)^{\lambda}}$for one or more sampling combinations (c₀, c₁, . . . , c_(r)). The totalarea covered is based at least on one or more sensing parameters or theportion of the area of interest. The overlap perimeter is a quantity ofthe sample that is redundant (e.g. overlapping) a previous sample.Lambda λ is an overlap exponential. The overlap exponential is a factorby which overlap between the sample and a previous sample is encouraged.For example, λ=0 may discourage overlap and encourage samplingcombinations including samples with large total areas covered. However,λ=0 may result in sampling combinations in which portions of the area ofinterest are uncaptured between the samples. As another example, λ>0 mayencourage overlap to ensure portions of the area of interest between thesamples (e.g. gaps) are captured. However, λ>0 may result in largequantities of samples, or translations of the sensor(s) or area ofinterest, for a given sampling combination to capture the area ofinterest.

In another embodiment, determining a sampling combination to be acquiredby the one or more sensors may include at (332) determining acombination of overlap exponentials based at least on a reinforcementlearning (RL) algorithm, at (334) calculating a score function for oneor more sampling combinations based at least on a total area covered bythe one or more sensors, an overlap perimeter, and the one or moreoverlap exponentials, and at (336) selecting the sampling combinationcorresponding to a maximum score function.

At (332), the method 300 may include using at least one of aState-Action-Result-State-Action (SARSA), Q-Learning, and PolicyGradient RL algorithm to determine a combination of overlap exponentialsthat may output a sampling combination that may minimize a quantity ofsamples taken of the area of interest pursuant to capturing the area ofinterest to a desired level of completeness. In various embodiments, atleast one of the Q-Learning and Policy Gradient PL algorithms may beused in conjunction with a deep learning approach to determine a minimalquantity of samples for capturing the area of interest at a desiredlevel of completeness. Determining the combination of overlapexponentials may include determining combinations of zero and non-zerooverlap exponentials that may result in a maximum score function whilecapturing the area of interest in a minimal quantity of samples to adesired level of completeness.

At (340), the method 300 includes acquiring the plurality of samplesusing the one or more sensors based at least on the samplingcombination. In one embodiment, acquiring the plurality of samples mayinclude translating the one or more sensor(s) and/or the area ofinterest relative to one another. For example, referring to FIG. 1, 2,or 4, the one or more sensors 110 and/or the area of interest 130 may bemounted to the robot 100 and translate to capture samples at a pluralityof portions 131 of the area of interest 130 until the area of interest130 is captured in desired detail and completeness. In anotherembodiment, the method 300 may be implemented to determine positions,placements, setups, orientations, distances, etc. of one or more sensorsrelative to the area of interest using the determined samplingcombination. For example, within the defined area of interest, such as a2D or 3D space, the determined sampling combination may providepositions, placements, and orientations of sensors such that a minimalquantity of sensors is utilized to capture the area of interest withinthe 2D or 3D space to a desired level of detail and completeness.

FIG. 4 depicts an example apparatus 90 according to exemplaryembodiments of the present disclosure. The apparatus 90 can include oneor more sensors 110, a robot 100, and one or more computing devices 120.In one embodiment, the robot 100 defines a robotic arm such as shown inregard to FIGS. 1 and 2. In another embodiment, such as shown in FIG. 4,the robot 100 defines an autonomous mobile vehicle, such as a drone. Theone or more sensors 120, the robot 100, and/or the computing device 120can be configured to communicate via more or more network(s) 410, whichcan include any suitable wired and/or wireless communication links fortransmission of the communications and/or data, as described herein. Forinstance, the network 410 can include a SATCOM network, ACARS network,ARINC network, SITA network, AVICOM network, a VHF network, a HFnetwork, a Wi-Fi network, a WiMAX network, a gatelink network, etc.

The computing device(s) 120 can include one or more processor(s) 121 andone or more memory device(s) 122. The one or more processor(s) 121 caninclude any suitable processing device, such as a microprocessor,microcontroller, integrated circuit, logic device, and/or other suitableprocessing device. The one or more memory device(s) 122 can include oneor more computer-readable media, including, but not limited to,non-transitory computer-readable media, RAM, ROM, hard drives, flashdrives, and/or other memory devices.

The one or more memory device(s) 122 can store information accessible bythe one or more processor(s) 121, including computer-readableinstructions 123 that can be executed by the one or more processor(s)121. The instructions 123 can be any set of instructions that whenexecuted by the one or more processor(s) 121, cause the one or moreprocessor(s) 121 to perform operations. In some embodiments, theinstructions 123 can be executed by the one or more processor(s) 121 tocause the one or more processor(s) 121 to perform operations, such asany of the operations and functions for which the computing device(s)120 are configured, the operations for sensor planning (e.g., method300), as described herein, the operations for defining or receiving anarea of interest, the operations for identifying or receiving one ormore sensing parameters for the one or more sensors, the operations fordetermining a sampling combination for acquiring a plurality of samplesby the one or more sensors based at least in part on the one or moresensing parameters, the operations for acquiring the plurality ofsamples of the area of interest using the one or more sensors based atleast on the sampling combination, and/or any other operations orfunctions of the one or more computing device(s) 120. The instructions123 can be software written in any suitable programming language or canbe implemented in hardware. Additionally, and/or alternatively, theinstructions 123 can be executed in logically and/or virtually separatethreads on processor(s) 121. The memory device(s) 122 can further storedata 124 that can be accessed by the processor(s) 121. For example, thedata 124 can include the one or more of the samples, the samplingcombinations, the sensing parameters, the defined area of interest, thescore function, the RL algorithm, the combinations of overlapexponentials, and/or any other data and/or information described herein.

The computing device(s) 120 can also include a network interface 125used to communicate, for example, with the other components of apparatus90 (e.g., via network 410). The network interface 125 can include anysuitable components for interfacing with one or more network(s),including for example, transmitters, receivers, ports, controllers,antennas, and/or other suitable components.

The technology discussed herein makes reference to computer-basedsystems and actions taken by and information sent to and fromcomputer-based systems. One of ordinary skill in the art will recognizethat the inherent flexibility of computer-based systems allows for agreat variety of possible configurations, combinations, and divisions oftasks and functionality between and among components. For instance,processes discussed herein can be implemented using a single computingdevice or multiple computing devices working in combination. Databases,memory, instructions, and applications can be implemented on a singlesystem or distributed across multiple systems. Distributed componentscan operate sequentially or in parallel.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they include structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

What is claimed is:
 1. A computer-implemented method of sensor planning for acquiring samples via an apparatus including one or more sensors, the computer-implemented method comprising: defining, by one or more computing devices, an area of interest; identifying, by the one or more computing devices, one or more sensing parameters for the one or more sensors; determining, by the one or more computing devices, a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters, wherein determining the sampling combination comprises: determining, by one or more computing devices, a combination of overlap exponentials based at least on a reinforcement learning algorithm; calculating, by one or more computing devices, a score function for one or more sampling combinations based at least on a total area covered by the one or more sensors, an overlap perimeter, and the overlap exponentials; and selecting, by one or more computing devices, the sampling combination corresponding to a maximum score function; and providing, by the one or more computing devices, one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination.
 2. The computer-implemented method of claim 1, wherein the reinforcement learning algorithm comprises using at least one of a SARSA, Q-Learning, and Policy Gradient reinforcement learning algorithm.
 3. The computer-implemented method of claim 1, wherein determining a combination of overlap exponentials based at least on a reinforcement learning algorithm comprises determining combinations of zero and non-zero overlap exponentials.
 4. The computer-implemented method of claim 1, wherein determining a sampling combination for acquiring a plurality of samples by the one or more sensors is based at least on a score function, wherein the score function is a function of at least one of a total area covered, an overlap perimeter, and an overlap exponential.
 5. The computer-implemented method of claim 1, wherein identifying one or more sensing parameters includes calculating a curvature and/or normal vector of at least a portion of the area of interest.
 6. The computer-implemented method of claim 1, wherein identifying one or more sensing parameters includes defining a total area covered by the one or more sensors.
 7. The computer-implemented method of claim 1, wherein identifying one or more sensing parameters includes defining one or more of a measurement resolution, a field of view, and/or a depth of field.
 8. The computer-implemented method of claim 1, wherein defining an area of interest includes receiving a point cloud.
 9. The computer-implemented method of claim 1, wherein defining an area of interest includes defining a finite space in which the one or more sensors operates.
 10. The computer-implemented method of claim 1, wherein acquiring the plurality of samples includes translating one or more sensors and/or translating the area of interest.
 11. The computer-implemented method of claim 1, wherein identifying one or more sensing parameters includes defining one or more translations of the one or more sensors.
 12. The computer-implemented method of claim 1, wherein the one or more sensors is an imaging device, a proximity sensor, or a combination thereof.
 13. A robotic sensing apparatus for sensor planning, the robotic sensing apparatus comprising one or more sensors and a computing device comprising one or more processors and one or more memory devices, the one or more memory devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising: receiving an area of interest; receiving one or more sensing parameters for the one or more sensors; determining a sampling combination for acquiring a plurality of samples by the one or more sensors, wherein determining the sampling combination comprises: determining a combination of overlap exponentials based at least on a reinforcement learning algorithm; calculating a score function for one or more sampling combinations based at least on a total area covered by the one or more sensors, an overlap perimeter, and the overlap exponentials; and selecting the sampling combination corresponding to a maximum score function; and acquiring the plurality of samples using the one or more sensors based at least on the sampling combination.
 14. The robotic sensing apparatus of claim 13, wherein determining a combination of overlap exponentials based at least on a reinforcement learning algorithm includes using a SARSA, Q-Learning, or Policy Gradient reinforcement learning algorithm.
 15. The robotic sensing apparatus of claim 13, wherein determining a combination of overlap exponentials based at least on a reinforcement learning algorithm includes determining combinations of zero and non-zero overlap exponentials.
 16. The robotic sensing apparatus of claim 13, wherein acquiring the plurality of samples includes translating one or more sensors and/or translating the area of interest.
 17. The robotic sensing apparatus of claim 13, wherein the one or more sensors is an imaging device, a proximity sensor, or a combination thereof.
 18. An apparatus for sensor planning, the apparatus comprising: a translating robotic apparatus; one or more sensors mounted to the translating robotic apparatus; and one or more computing devices configured to operate the translating robotic apparatus and the one or more sensors, wherein the computing device stores operations, the operations comprising: defining an area of interest; identifying one or more sensing parameters for the one or more sensors; determining a sampling combination for acquiring a plurality of samples by the one or more sensors based at least in part on the one or more sensing parameters, wherein determining the sampling combination comprises: determining a combination of overlap exponentials based at least on a reinforcement learning algorithm; calculating a score function for one or more sampling combinations based at least on a total area covered by the one or more sensors, an overlap perimeter, and the overlap exponentials; and selecting the sampling combination corresponding to a maximum score function; and providing one or more command control signals to the apparatus including the one or more sensors to acquire the plurality of samples of the area of interest using the one or more sensors based at least on the sampling combination. 